用CNN+LSTM实时检测视频异常，提升安防响应速度。
Real time anomalies detection on video
- CNN提取视频特征，形成时序数据由LSTM/GRU分析
- 实现视频流的实时异常检测，无需事后回放
- 适合需要即时响应的安防场景
当前许多场所使用安全摄像头，但通常仅用于事件发生后的回溯，难以发挥实时检测作用。本文提出一种基于深度学习的实时视频异常检测方法：利用卷积神经网络（CNN）提取视频帧的特征，生成时间序列，再通过长短期记忆网络（LSTM）或门控循环单元（GRU）进行分析，实现对视频流中异常行为的即时识别，弥补传统监控系统在事前预警方面的不足。
原文摘要 · Abstract (English)
Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models.
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